Many on-line, handwriting recognition systems employ curve matching methods to match an unknown character against prototype, or template, characters. Examples of such systems are described in the following articles: W. Doster and R. Oed, "Word processing with on-line script recognition", IEEE Micro, Vol. 4, pp. 36-43, October 1984; K. Ikeda, T. Yamamura, Y. Mitamura, S. Fujiwara, Y. Tominaga, and T. Kiyono, "On-line recognition of handwritten characters utilizing positional and stroke vector sequences", Proc. 4th Int. Jt. Conf. Pattern Recognition, pp. 813-815, November 1978; C. C. Tappert, "Adaptive on-line handwriting recognition", Proc. 7th Int. Conf. Pattern Recognition, pp. 1004-1007, 1984; C. C. Tappert, "Speed, accuracy, flexibility trade-offs in on-line character recognition", IBM Research Report RC13228, October 1987; and T. Wakahara and M. Umeda, "Stroke-number and stroke-order free on-line character recognition by selective stroke linkage method", Proc. 4th ICTP, pp. 157-162, 1983. In general, the recognition accuracy of such prototype-based handwriting recognition systems is a function of the quality of the prototypes. Many online, handwriting recognition systems use elastic curve matching to match an unknown character against prototype (template) characters. Examples of such systems are described in the following articles: T. Fujisaki, T. E. Chefalas, J. Kim, and C. C. Tappert, "Online recognizer for runon handprinted characters", Proc. 10th Int. Conf. Pattern Recognition, pp. 450-454, June 1990; K. Ikeda, T. Yamamura, Y. Mitamura, S. Fujiwara, Y. Tominaga, and T. Kiyono, "Online recognition of handwritten characters utilizing positional and stroke vector sequences", Proc. 4th Int. Conf. Pattern Recognition, pp. 813-815, November 1978; C. C. Tappert, "Adaptive online handwriting recognition", Proc. 7th Int. Conf. Pattern Recognition, pp. 1004-1007, 1984. Such systems usually represent each way of writing a character by a single prototype that usually is one writing of the character. This minimizes the number of prototypes and therefore the computation time for matching.
The recognition system of T. Fijisaki et al, above, collects original character prototypes from a user's writing samples through a training scenario. Averaged prototypes are formed by averaging original character prototypes of the same label and shape (within a match threshold). For example, similarly-shaped A's are averaged to yield an averaged A prototype.
An article by T. E. Chefalas and C. C. Tappert, "Improved prototype establishment in a handwriting recognition system", IBM Tech. Disclosure Bulletin, Vol. 33, p. 420, January 1991 describes a technique for global and incremental averaging techniques for online handwriting recognition.
An article by J. M. Kurtzberg and C. C. Tappert, "Symbol Recognition System By Elastic Matching", IBM Tech. Disclosure Bulletin, Vol. 24, No. 6, pp. 2897-2902, November 1981, describes a technique for utilizing elastic matching to recognize symbols.
An article by C. C. Tappert, "Cursive Script Recognition System By Elastic Matching", IBM Tech. Disclosure Bulletin, Vol. 24, No. 11A, pp. 5404-5407 describes a technique for utilizing elastic matching to recognize cursive script.
According to this invention, an elastic-matching (dynamic programming) procedure is not used for recognition as described above, but rather is utilized to improve the alignment of the parametric representation of already recognized characters to be averaged to produce an averaged prototype character. The point-to-point correspondence resulting from an elastic match of two characters is obtained by using backpointers during the calculation of the match. This improved elastic method of alignment is applicable to both global and incremental averaging techniques described above.